
Actigraphy-based step analysis for the detection of depressed mood: An explainable machine learning approach.
Actigraphy data reveals AI’s potential in detecting depression: 0.679-0.833 AUROC accuracy across demographics. ๐๐ง
Discover the newest research about AI innovations in ๐ง Mental Health.
Actigraphy data reveals AI’s potential in detecting depression: 0.679-0.833 AUROC accuracy across demographics. ๐๐ง
Evaluating ASR in clinical settings: WER ranges from 0.31 to 0.58 in schizophrenia samples. Context matters! ๐๐ง
Exploring ethical implications of GenAI in mental health: accessibility, risks, and strategic assessment tools. ๐ค๐ง
Large language models show promise in mental health assessment and diagnosis. Key findings from our review of a PubMed article reveal significant performance differences. ๐๐ง
Brain imaging may help identify young adults who could benefit from an anxiety care app. ๐ง ๐ฑ
Innovative VR, AI, and voice tech enhance Alzheimer’s care, boosting cognitive function and social interaction. ๐๐ง
Exploring AI’s impact on emergency nursing support during explosive attacks: key findings from Seyedin et al. ๐๐ค
Passive sensing & ML enhance mental health monitoring. 42 studies show 92.16% accuracy in anxiety detection. ๐๐ง
New digital platform launched to address health disparities. ๐ HARP provides data and resources for healthcare organizations. ๐ฅ
AI enhances mental health for aging populations, addressing loneliness and cognitive decline. Challenges include trust and cultural sensitivity. ๐ค๐ก